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| A Monte Carlo method for in silico modeling and visualization of Waddington's epigenetic landscape with intermediate details | |
| 2020-12 | |
| Source Publication | BIOSYSTEMS
 |
| ISSN | 0303-2647 |
| EISSN | 1872-8324 |
| Volume | 198Pages:#VALUE! |
| DOI | 10.1016/j.biosystems.2020.104275 |
| Abstract | Waddington's epigenetic landscape is a classic metaphor for describing the cellular dynamics during the development modulated by gene regulation. Quantifying Waddington's epigenetic landscape by mathematical modeling would be useful for understanding the mechanisms of cell fate determination. A few computational methods have been proposed for quantitative modeling of landscape; however, to model and visualize the landscape of a high dimensional gene regulatory system with realistic details is still challenging. Here, we propose a Monte Carlo method for modeling the Waddington's epigenetic landscape of a gene regulatory network (GRN). The method estimates the probability distribution of cellular states by collecting a large number of time-course simulations with random initial conditions. By projecting all the trajectories into a 2-dimensional plane of dimensions i and j, we can approximately calculate the quasi-potential U(x(i), x(j), *) = -ln P(x(i,) x(j), *), where P(x(i), x(j), *) is the estimated probability of an equilibrium steady state or a non-equilibrium state. Compared to the state-of-the-art methods, our Monte Carlo method can quantify the global potential landscape (or emergence behavior) of GRN for a high dimensional system. The potential landscapes show that not only attractors represent stability, but the paths between attractors are also part of the stability or robustness of biological systems. We demonstrate the novelty and reliability of our method by plotting the potential landscapes of a few published models of GRN. |
| Keyword | Waddington's epigenetic landscape Monte Carlo Gene regulatory network Dynamical systems |
| URL | 查看原文 |
| Indexed By | SCI ; SCIE |
| Language | 英语 |
| WOS Research Area | Life Sciences & Biomedicine - Other Topics ; Mathematical & Computational Biology |
| WOS Subject | Biology ; Mathematical & Computational Biology |
| WOS ID | WOS:000595260100012 |
| Publisher | ELSEVIER SCI LTD |
| WOS Keyword | STOCHASTIC SIMULATION ; POTENTIAL LANDSCAPE ; PLURIPOTENT ; FRAMEWORK ; DYNAMICS |
| Original Document Type | Article |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/125965 |
| Collection | 信息科学与技术学院_硕士生 信息科学与技术学院_PI研究组_郑杰组 |
| Corresponding Author | Zheng, Jie |
| Affiliation | 1.Nanyang Technol Univ, Sch Comp Sci & Engn, Biomed Informat Lab, Singapore 639798, Singapore; 2.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China |
| Corresponding Author Affilication | School of Information Science and Technology |
| Recommended Citation GB/T 7714 | Zhang, Xiaomeng,Chong, Ket Hing,Zhu, Lin,et al. A Monte Carlo method for in silico modeling and visualization of Waddington's epigenetic landscape with intermediate details[J]. BIOSYSTEMS,2020,198:#VALUE!. |
| APA | Zhang, Xiaomeng,Chong, Ket Hing,Zhu, Lin,&Zheng, Jie.(2020).A Monte Carlo method for in silico modeling and visualization of Waddington's epigenetic landscape with intermediate details.BIOSYSTEMS,198,#VALUE!. |
| MLA | Zhang, Xiaomeng,et al."A Monte Carlo method for in silico modeling and visualization of Waddington's epigenetic landscape with intermediate details".BIOSYSTEMS 198(2020):#VALUE!. |
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